The rapid growth of applications that rely on artificial neural network (ANN) concepts gives rise to a staggering increase in the demand for hardware implementations of neural networks. New types of hardware that can support the requirements of high-speed associative computing while maintaining low power consumption are sought, and optical artificial neural networks fit the task well. Inherently, optical artificial neural networks can be faster, support larger bandwidth, and produce less heat than their electronic counterparts. Here we propose the design of an optical ANN-based imaging system that has the ability to self-study image signals from an incoherent light source in different colors. Our design consists of a combination of a multimode fiber and a multi-core optical fiber realizing a neural network. We show that the signals, transmitted through the multimode fiber, can be used for image identification purposes and can also be reconstructed using ANNs with a low number of nodes. An all-optical solution can then be achieved by realizing these networks with the multi-core optical neural network fiber.

中文翻译：

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在多模光纤图像上使用人工神经网络进行彩色图像识别和重建：朝着全光学设计的方向发展

依赖人工神经网络（ANN）概念的应用程序的快速增长，导致对神经网络的硬件实现的需求急剧增加。寻求能够在保持低功耗的同时支持高速关联计算需求的新型硬件，并且光学人工神经网络很好地满足了这一任务。从本质上讲，光学人工神经网络可以比电子人工神经网络更快，支持更大的带宽并产生更少的热量。在这里，我们提出了一种基于光学ANN的成像系统的设计，该系统具有自学习来自不同颜色的非相干光源的图像信号的能力。我们的设计由实现神经网络的多模光纤和多芯光纤的组合组成。我们显示出这些信号 通过多模光纤传输的信号可以用于图像识别，也可以使用节点数少的ANN进行重建。然后，可以通过使用多核光学神经网络光纤实现这些网络来实现全光学解决方案。